Relevance weighting of search terms
Document retrieval systems
Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance based language models
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Learning to rank using gradient descent
ICML '05 Proceedings of the 22nd international conference on Machine learning
A bayesian logistic regression model for active relevance feedback
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Improving relevance feedback in language modeling with score regularization
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Document selection methodologies for efficient and effective learning-to-rank
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Semi-supervised learning to rank with preference regularization
Proceedings of the 20th ACM international conference on Information and knowledge management
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We incorporate relevance feedback into a learning to rank framework by exploiting query-specific document similarities. Given a few judged feedback documents and many retrieved but unjudged documents for a query, we learn a function that adjusts the initial ranking score of each document. Scores are fit so that documents with similar term content get similar scores, and scores of judged documents are close to their labels. By such smoothing along the manifold of retrieved documents, we avoid overfitting, and can therefore learn a detailed query-specific scoring function with several dozen term weights.